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About this Publication
Title
Generalizability and Transportability of the National Lung Screening Trial data: Extending trial results to different populations.
Pubmed ID
34548326 (View this publication on the PubMed website)
Digital Object Identifier
Publication
Cancer Epidemiol Biomarkers Prev. 2021 Sep 20
Authors

Inoue K, Hsu W, Arah OA, Prosper AE, Aberle DR, Bui AAT

Abstract

BACKGROUND: Randomized controlled trials (RCTs) play a central role in evidence-based healthcare. However, the clinical and policy implications of implementing RCTs in clinical practice are difficult to predict as the studied population is often different from the target population where results are being applied. This study illustrates the concepts of generalizability and transportability, demonstrating their utility in interpreting results from the National Lung Screening Trial (NLST).

METHODS: Using inverse-odds weighting, we demonstrate how generalizability and transportability techniques can be used to extrapolate treatment effect from (i) a subset of NLST to the entire NLST population and from (ii) the entire NLST to different target populations.

RESULTS: Our generalizability analysis revealed that lung cancer mortality reduction by LDCT screening across the entire NLST (16% [95% CI: 4-24]) could have been estimated using a smaller subset of NLST participants. Using transportability analysis, we showed that populations with a higher prevalence of females and current smokers had a greater reduction in lung cancer mortality with LDCT screening (e.g., 27% [95% CI: 11-37] for the population with 80% females and 80% current smokers) than those with lower prevalence of females and current smokers.

CONCLUSIONS: This article illustrates how generalizability and transportability methods extend estimation of RCTs' utility beyond trial participants, to external populations of interest including those that more closely mirror real-world populations.

IMPACT: Generalizability and transportability approaches can be used to quantify treatment effects for populations of interest, which may be used to design future trials or adjust lung cancer screening eligibility criteria.

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